Reverse Rebalancing and the Volatility Tax: Why Chasing Winners Loses to 1/n Equal-Weight Rebalancing

This technical note starts from a deliberately strict axiom: at the chosen short horizon, returns are not forecastable (a no-forecast stance). Under this axiom, the key decision is not “what to predict,” but “how portfolio weights are updated after prices move.” We parameterize weight-updating rules by a single feedback exponent β, spanning constant-weight equal-weight discipline, buy-and-hold drift, and procyclical “reverse rebalancing” (chasing recent winners and selling recent losers). Using a discrete-time log-wealth identity, we decompose relative log growth into (i) an exposure/drift component, (ii) a concavity (Jensen/AM–GM) component generated by contrarian rebalancing, and (iii) implementation losses from turnover, convex market impact, taxes, and non-tradability constraints. Reverse rebalancing forfeits the concavity component and can be interpreted as paying a “volatility tax” for convex behavior when no forecasting edge exists. We connect the mechanism to high-quality evidence on the robustness of naive 1/n rules (DeMiguel, Garlappi, and Uppal, 2009) and to decompositions showing that a large share of equal-weight outperformance is attributable to rebalancing itself rather than purely to size tilt (Plyakha, Uppal, and Vilkov, 2012). Finally, we outline a reproducible U.S.–China roadmap (e.g., RSP–SPY; CSI 500 equal-weight vs cap-weight) and highlight market-specific failure modes, especially China A-shares’ price limits and suspensions. The note’s message is pedagogical but operational: for ordinary investors, disciplined rules can constitute a practical form of “alpha” by systematically avoiding self-inflicted convexity losses. Archived version (Zenodo DOI): 10.5281/zenodo.18638385